首页> 外文会议>International Conference on Computational Intelligence >Missing Values Imputation Using Fuzzy C Means Based On Correlation of Variable
【24h】

Missing Values Imputation Using Fuzzy C Means Based On Correlation of Variable

机译:基于变量相关的模糊C均值缺失值插补

获取原文

摘要

Missing values is one of the problems in real-world data and an unavoidable one. It should be handled carefully in a pre-processing technique before being processed in a data mining technique. This paper proposes an imputation technique of Fuzzy C Mean (FCM) with the improved version. The aim is to reduce errors and increase the accuracy of the processing technique. In this paper, the correlation technique was applied before the process of FCM to choose the variables with a certain criterion to be processed in FCM imputation. The result shows that the proposed technique outperforms the conventional technique and useful to overcome the disadvantages of the FCM technique.
机译:缺少值是现实数据中的问题之一,也是不可避免的问题。在数据挖掘技术中对其进行处理之前,应先对其进行仔细处理。本文提出了一种改进的模糊C均值(FCM)归因技术。目的是减少错误并提高处理技术的准确性。本文采用相关技术,在FCM处理之前,采用一定的准则选择变量进行FCM插补处理。结果表明,所提出的技术优于传统技术,对于克服FCM技术的弊端非常有用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号